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Murray, Paul
in Cooperation with on an Cooperation-Score of 37%
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Publications (11/11 displayed)
- 2024CNN-based automated approach to crack-feature detection in steam cycle componentscitations
- 2023Deep learning enhanced Watershed for microstructural analysis using a boundary class semantic segmentationcitations
- 2023Passive gamma-ray analysis of UO2 fuel rods using SrI2(Eu) scintillators in multi-detector arrangements
- 2022X-ray classification of Special Nuclear Materials using image segmentation and feature descriptors
- 2020Design of 2D sparse array transducers for anomaly detection in medical phantomscitations
- 2017Automated microstructural analysis of titanium alloys using digital image processingcitations
- 2016Use of hyperspectral imaging for artwork authentication
- 2015Automated image stitching for fuel channel inspection of AGR cores
- 2013Automated image stitching for enhanced visual inspections of nuclear power stations
- 2012A review of recent advances in the hit-or-miss transformcitations
- 2011A fast method for computing the output of rank order filters within arbitrarily shaped windows
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article
CNN-based automated approach to crack-feature detection in steam cycle components
Abstract
Periodic manual inspection by trained specialists is an important element of asset management in the nuclear industry. Detection of cracks caused by stress corrosion is an important element of remote visual inspection (RVI) in power plant steam generator components such as boilers, superheaters and reheaters. Challenges exist in the interpretation of RVI footage, such as high degree of concentration for reviewing lengthy and disorienting footage due to narrow field of view offered by endoscope. Deep learning is considered useful to automate crack detection process for improved efficiency and accuracy, and has enjoyed success in related applications. This article utilises a new application of automated crack feature detection in steam cycle components to demonstrate a transferrable data-driven framework for a variety of anomaly inspections in such structures. Specifically, a case study of superheater (a type of reactor pressure vessel head) anomaly inspection is presented to automatically detect regions of crack-like features in inspection footage with a good accuracy of 92.97% using convolutional neural network (CNN), even in challenging cases. Due to the black-box nature of the CNN classification, the explicability of the classification results is discussed to enhance the trustworthiness of the detection system.